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The Gillis–Domb–Fisher correlated random walk

Published online by Cambridge University Press:  14 July 2016

Anyue Chen*
Affiliation:
University of Edinburgh
Eric Renshaw*
Affiliation:
University of Strathclyde
*
Postal address: Department of Statistics, James Clerk Maxwell Building, King's Buildings, University of Edinburgh, Mayfield Road, Edinburgh EH9 3JZ, UK.
∗∗ Postal address: Department of Statistics and Modelling Science, Livingstone Tower, University of Strathclyde, 26 Richmond Street, Glasgow G1 1XH, UK.

Abstract

Correlated random walk models figure prominently in many scientific disciplines. Of fundamental importance in such applications is the development of the characteristic function of the n-step probability distribution since it contains complete information on the probability structure of the process. Using a simple algebraic lemma we derive the n-step characteristic function of the Gillis correlated random walk together with other related results. In particular, we present a new and simple proof of Gillis's conjecture, consider the generalization to the Gillis–Domb–Fisher walk, and examine the effect of including an arbitrary initial distribution.

Type
Research Papers
Copyright
Copyright © Applied Probability Trust 1992 

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References

Barber, M. N. and Ninham, B. W. (1970) Random and Restricted Walks. Gordon and Breach, New York.Google Scholar
Domb, C. and Fisher, M. E. (1958) On random walks with restricted reversals. Proc. Camb. Phil. Soc. 54, 4859.Google Scholar
Flory, P. J. (1962) Principles of Polymer Chemistry. Cornell University Press, Ithaca, NY.Google Scholar
Gillis, J. (1955) Correlated random walk. Proc. Camb. Phil. Soc. 51, 639651.Google Scholar
Henderson, R. (1981) The structural root systems of Sitka spruce and related stochastic processes. , University of Edinburgh.Google Scholar
Henderson, R. and Renshaw, E. (1980) Spatial stochastic models and computer simulation applied to the study of tree root systems. Compstat 80, 389395. Physica Verlag, Vienna.Google Scholar
Henderson, R., Ford, E. D., Renshaw, E. and Deans, J. D. (1983a) Morphology of the structural root system of Sitka spruce, 1: Analysis and quantitative description. Forestry 56, 121135.CrossRefGoogle Scholar
Henderson, R., Ford, E. D. and Renshaw, E. (1983b) Morphology of the structural root system of Sitka spruce, 2: Computer simulation of rooting patterns. Forestry 56, 137153.Google Scholar
Henderson, R., Renshaw, E. and Ford, E. D. (1984) A correlated random walk model for two-dimensional diffusion. J. Appl. Prob. 21, 233246.CrossRefGoogle Scholar
Horn, R. A. and Johnson, C. A. (1985) Matrix Analysis. Cambridge University Press.Google Scholar
Iossif, G. (1986) Return probabilities for correlated random walks. J. Appl. Prob. 23, 201207.Google Scholar
Jain, G. C. (1971) Some results in a correlated random walk. Canad. Math. Bull. 14, 341347.Google Scholar
Jain, G. C. (1973) On the expected number of visits of a particle before absorption in a correlated random walk. Canad. Math. Bull. 16, 389395.CrossRefGoogle Scholar
Jakeman, E. (1984) Speckle statistics with a small number of scatterers. Opt. Eng. 23, 453461.Google Scholar
Jakeman, E. and Renshaw, E. (1987) Correlated random-walk model for scattering. J. Opt. Soc. Amer. A4, 12061212.Google Scholar
Pólya, G. (1921) Uber eine Aufgabe der Wahrscheinlichkeitsrechnung betreffend die Irrfahrt im Straszennetz. Math. Ann. 84, 149160.Google Scholar
Renshaw, E. (1985) Computer simulation of Sitka spruce: Spatial branching models for canopy growth and root structure. IMA J. Math. Appl. Med. Biol. 2, 183200.Google Scholar
Renshaw, E. and Henderson, R. (1981) The correlated random walk. J. Appl. Prob 18, 403414.CrossRefGoogle Scholar
Seth, A. (1963) The correlated unrestricted random walk. J. R. Statist. Soc. B25, 394400.Google Scholar
Skellam, J. G. (1973) The formulation and interpretation of mathematical models of diffusionary processes in population biology. In The Mathematical Theory of the Dynamics of Biological Populations, ed. Bartlett, M. S. and Hiorns, R. W., pp. 6385, Academic Press, London.Google Scholar
Spiegel, M. R. (1971) Finite Differences and Difference Equations. McGraw-Hill, New York.Google Scholar
Spitzer, F. (1964) Principles of Random Walk. Van Nostrand, Princeton, NJ.Google Scholar